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From: marty1@houdi.UUCP (M.BRILLIANT)
Newsgroups: comp.ai,comp.cog-eng
Subject: Re: The symbol grounding problem
Message-ID: <1171@houdi.UUCP>
Date: Fri, 19-Jun-87 22:17:09 EDT
Article-I.D.: houdi.1171
Posted: Fri Jun 19 22:17:09 1987
Date-Received: Fri, 26-Jun-87 05:02:45 EDT
References: ... <770@mind.UUCP> <6174@diamond.BBN.COM> <1166@houdi.UUCP> <861@mind.UUCP>
Organization: AT&T Bell Laboratories, Holmdel
Lines: 76
Summary: Key to the solution is the feature extractor
Xref: mnetor comp.ai:567 comp.cog-eng:144

In article <861@mind.UUCP>, harnad@mind.UUCP writes:
> marty1@houdi.UUCP (M.BRILLIANT) asks:
> 
> >	what do you think is essential: (A) literally analog transformation,
> >	(B) invertibility, or (C) preservation of significant relational
> >	functions?
> 
Let me see if I can correctly rephrase his answer:

(i) "discrimination" (pairwise same/different judgments) he associates
with iconic ("analog") representations, which he says have to be
invertible, and will ordinarily be really analog because "dedicated"
digital equivalents will be too complex.

(ii) for "identification" or "categorization" (sorting and labeling of
objects), he says only distinctive features need be extracted from the
sensory projection; this process is not invertible.

(iii) for "conscious problem-solving," etc., he says relation-preserving
symbolic representations would be optimal, if they are not "autonomous
(modular)" but rather are grounded by deriving their atomic symbols
through the categorization process above.

(iv) to pass the Total Turing Test he wants all of the above, tied
together in the sequence described.

I agree with this formulation in most of its terms.  But some of the
terms are confusing, in that if I accept what I think are good
definitions, I don't entirely agree with the statements above.

"Invertible/Analog": The property of invertibility is easy to visualize
for continuous functions. First, continuous functions are what I would
call "analog" transformations.  They are at least locally image-forming
(iconic). Then, saying a continuous transformation is invertible, or
one-to-one, means it is monotonic, like a linear transformation, rather
than many-to-one like a parabolic transformation.  That is, it is
unambiguously iconic.

It might be argued that physical sensors can be ambiguously iconic,
e.g., an object seen in a half-silvered mirror.  Harnad would argue
that the ambiguity is inherent in the physical scene, and is not
dependent on the sensor.  I would agree with that if no human sensory
system ever gave ambiguous imaging of unambiguous objects.  What about
the ambiguity of stereophonic location of sound sources?  In that case
the imaging (i) is unambiguous; only the perception (ii) is ambiguous.

But physical sensors are also noisy.  In mathematical terms, that noise
could be modeled as discontinuity, as many-to-one, as one-to-many, or
combinations of these.  The noisy transformation is not invertible. 
But a "physically analog" sensory process (as distinct from a digital
one) can be approximately modeled (to within the noise) by a continuous
transformation.  The continuous approximation allows us to regard the
analog transformation as image-forming (iconic).  But only the
continuous approximation is invertible.

"Autonomous/Modular": The definition of "modular" is not clear to me. 
I have Harnad's definition "not analogous to a top-down, autonomous
symbol-crunching module ... hardwired to peripheral modules."  The
terms in the definition need defining themselves, and I think there are
too many of them.

I would rather look at the "hybrid" three-layer system and say it does
not have a "symbol-cruncher hardwired to peripheral modules" because
there is a feature extractor (and classifier) in between.  The main
point is the presence or absence of the feature extractor.

The symbol-grounding problem arises because the symbols are discrete,
and therefore have to be associated with discrete objects or classes.
Without the feature extractor, there would be no way to derive discrete
objects from the sensory inputs.  The feature extractor obviates the
symbol-grounding problem.  I consider the "symbol-cruncher hardwired to
peripheral modules" to be not only a straw man but a dead horse.

M. B. Brilliant					Marty
AT&T-BL HO 3D-520	(201)-949-1858
Holmdel, NJ 07733	ihnp4!houdi!marty1